Overview

Dataset statistics

Number of variables8
Number of observations500
Missing cells7
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory302.8 KiB
Average record size in memory620.2 B

Variable types

Text3
Numeric5

Alerts

Length_of_Membership is highly overall correlated with Yearly_Amount_SpentHigh correlation
Yearly_Amount_Spent is highly overall correlated with Length_of_MembershipHigh correlation
Email has unique valuesUnique
Address has unique valuesUnique
Avg._Session_Length has unique valuesUnique
Length_of_Membership has unique valuesUnique
Yearly_Amount_Spent has unique valuesUnique

Reproduction

Analysis started2023-09-07 17:41:16.921388
Analysis finished2023-09-07 17:43:48.647376
Duration2 minutes and 31.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Email
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size38.7 KiB
2023-09-07T14:43:48.800379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length34
Mean length21.962
Min length13

Characters and Unicode

Total characters10981
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)100.0%

Sample

1st rowmstephenson@fernandez.com
2nd rowhduke@hotmail.com
3rd rowpallen@yahoo.com
4th rowriverarebecca@gmail.com
5th rowmstephens@davidson-herman.com
ValueCountFrequency (%)
mstephenson@fernandez.com 1
 
0.2%
jstark@anderson.com 1
 
0.2%
pallen@yahoo.com 1
 
0.2%
riverarebecca@gmail.com 1
 
0.2%
mstephens@davidson-herman.com 1
 
0.2%
alvareznancy@lucas.biz 1
 
0.2%
katherine20@yahoo.com 1
 
0.2%
awatkins@yahoo.com 1
 
0.2%
vchurch@walter-martinez.com 1
 
0.2%
bonnie69@lin.biz 1
 
0.2%
Other values (490) 490
98.0%
2023-09-07T14:43:49.233379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1098
 
10.0%
a 980
 
8.9%
m 804
 
7.3%
e 738
 
6.7%
n 648
 
5.9%
c 632
 
5.8%
i 630
 
5.7%
r 619
 
5.6%
l 603
 
5.5%
. 500
 
4.6%
Other values (29) 3729
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9627
87.7%
Other Punctuation 1000
 
9.1%
Decimal Number 270
 
2.5%
Dash Punctuation 84
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1098
11.4%
a 980
10.2%
m 804
 
8.4%
e 738
 
7.7%
n 648
 
6.7%
c 632
 
6.6%
i 630
 
6.5%
r 619
 
6.4%
l 603
 
6.3%
h 462
 
4.8%
Other values (16) 2413
25.1%
Decimal Number
ValueCountFrequency (%)
8 39
14.4%
2 36
13.3%
6 31
11.5%
5 30
11.1%
0 28
10.4%
4 27
10.0%
9 26
9.6%
1 19
7.0%
3 19
7.0%
7 15
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 500
50.0%
@ 500
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9627
87.7%
Common 1354
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1098
11.4%
a 980
10.2%
m 804
 
8.4%
e 738
 
7.7%
n 648
 
6.7%
c 632
 
6.6%
i 630
 
6.5%
r 619
 
6.4%
l 603
 
6.3%
h 462
 
4.8%
Other values (16) 2413
25.1%
Common
ValueCountFrequency (%)
. 500
36.9%
@ 500
36.9%
- 84
 
6.2%
8 39
 
2.9%
2 36
 
2.7%
6 31
 
2.3%
5 30
 
2.2%
0 28
 
2.1%
4 27
 
2.0%
9 26
 
1.9%
Other values (3) 53
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1098
 
10.0%
a 980
 
8.9%
m 804
 
7.3%
e 738
 
6.7%
n 648
 
5.9%
c 632
 
5.8%
i 630
 
5.7%
r 619
 
5.6%
l 603
 
5.5%
. 500
 
4.6%
Other values (29) 3729
34.0%

Address
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
2023-09-07T14:43:49.515347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length55
Mean length46.956
Min length23

Characters and Unicode

Total characters23478
Distinct characters66
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)100.0%

Sample

1st row835 Frank Tunnel Wrightmouth, MI 82180-9605
2nd row4547 Archer Common Diazchester, CA 06566-8576
3rd row24645 Valerie Unions Suite 582 Cobbborough, DC 99414-7564
4th row1414 David Throughway Port Jason, OH 22070-1220
5th row14023 Rodriguez Passage Port Jacobville, PR 37242-1057
ValueCountFrequency (%)
apt 125
 
3.4%
suite 117
 
3.1%
lake 43
 
1.2%
north 37
 
1.0%
port 33
 
0.9%
box 33
 
0.9%
south 32
 
0.9%
new 31
 
0.8%
east 30
 
0.8%
west 28
 
0.8%
Other values (2137) 3220
86.4%
2023-09-07T14:43:49.999378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2729
 
11.6%
e 1233
 
5.3%
a 920
 
3.9%
t 915
 
3.9%
r 863
 
3.7%
o 734
 
3.1%
i 712
 
3.0%
3 674
 
2.9%
7 653
 
2.8%
6 653
 
2.8%
Other values (56) 13392
57.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9874
42.1%
Decimal Number 6383
27.2%
Uppercase Letter 3133
 
13.3%
Space Separator 2729
 
11.6%
Other Punctuation 595
 
2.5%
Control 500
 
2.1%
Dash Punctuation 264
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1233
12.5%
a 920
9.3%
t 915
9.3%
r 863
 
8.7%
o 734
 
7.4%
i 712
 
7.2%
n 649
 
6.6%
s 617
 
6.2%
l 503
 
5.1%
h 445
 
4.5%
Other values (16) 2283
23.1%
Uppercase Letter
ValueCountFrequency (%)
A 379
 
12.1%
S 347
 
11.1%
M 240
 
7.7%
P 218
 
7.0%
C 185
 
5.9%
N 177
 
5.6%
L 138
 
4.4%
D 127
 
4.1%
J 125
 
4.0%
R 120
 
3.8%
Other values (15) 1077
34.4%
Decimal Number
ValueCountFrequency (%)
3 674
10.6%
7 653
10.2%
6 653
10.2%
2 647
10.1%
1 643
10.1%
0 640
10.0%
4 630
9.9%
8 624
9.8%
9 621
9.7%
5 598
9.4%
Other Punctuation
ValueCountFrequency (%)
, 470
79.0%
. 125
 
21.0%
Space Separator
ValueCountFrequency (%)
2729
100.0%
Control
ValueCountFrequency (%)
500
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 264
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13007
55.4%
Common 10471
44.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1233
 
9.5%
a 920
 
7.1%
t 915
 
7.0%
r 863
 
6.6%
o 734
 
5.6%
i 712
 
5.5%
n 649
 
5.0%
s 617
 
4.7%
l 503
 
3.9%
h 445
 
3.4%
Other values (41) 5416
41.6%
Common
ValueCountFrequency (%)
2729
26.1%
3 674
 
6.4%
7 653
 
6.2%
6 653
 
6.2%
2 647
 
6.2%
1 643
 
6.1%
0 640
 
6.1%
4 630
 
6.0%
8 624
 
6.0%
9 621
 
5.9%
Other values (5) 1957
18.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23478
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2729
 
11.6%
e 1233
 
5.3%
a 920
 
3.9%
t 915
 
3.9%
r 863
 
3.7%
o 734
 
3.1%
i 712
 
3.0%
3 674
 
2.9%
7 653
 
2.8%
6 653
 
2.8%
Other values (56) 13392
57.0%

Avatar
Text

Distinct138
Distinct (%)27.8%
Missing3
Missing (%)0.6%
Memory size32.2 KiB
2023-09-07T14:43:50.248377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length8.7867203
Min length3

Characters and Unicode

Total characters4367
Distinct characters45
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)3.2%

Sample

1st rowViolet
2nd rowDarkGreen
3rd rowBisque
4th rowSaddleBrown
5th rowMediumAquaMarine
ValueCountFrequency (%)
teal 7
 
1.4%
greenyellow 7
 
1.4%
cadetblue 7
 
1.4%
cyan 7
 
1.4%
slateblue 7
 
1.4%
darkgoldenrod 6
 
1.2%
deeppink 6
 
1.2%
bisque 6
 
1.2%
saddlebrown 6
 
1.2%
orange 6
 
1.2%
Other values (128) 432
86.9%
2023-09-07T14:43:50.698347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 591
 
13.5%
a 323
 
7.4%
i 289
 
6.6%
r 277
 
6.3%
l 276
 
6.3%
n 258
 
5.9%
o 217
 
5.0%
t 183
 
4.2%
u 181
 
4.1%
d 139
 
3.2%
Other values (35) 1633
37.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3498
80.1%
Uppercase Letter 869
 
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 591
16.9%
a 323
9.2%
i 289
 
8.3%
r 277
 
7.9%
l 276
 
7.9%
n 258
 
7.4%
o 217
 
6.2%
t 183
 
5.2%
u 181
 
5.2%
d 139
 
4.0%
Other values (15) 764
21.8%
Uppercase Letter
ValueCountFrequency (%)
B 114
13.1%
G 103
11.9%
S 99
11.4%
D 86
9.9%
L 68
7.8%
M 60
 
6.9%
C 49
 
5.6%
P 48
 
5.5%
O 37
 
4.3%
R 36
 
4.1%
Other values (10) 169
19.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 4367
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 591
 
13.5%
a 323
 
7.4%
i 289
 
6.6%
r 277
 
6.3%
l 276
 
6.3%
n 258
 
5.9%
o 217
 
5.0%
t 183
 
4.2%
u 181
 
4.1%
d 139
 
3.2%
Other values (35) 1633
37.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4367
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 591
 
13.5%
a 323
 
7.4%
i 289
 
6.6%
r 277
 
6.3%
l 276
 
6.3%
n 258
 
5.9%
o 217
 
5.0%
t 183
 
4.2%
u 181
 
4.1%
d 139
 
3.2%
Other values (35) 1633
37.4%

Avg._Session_Length
Real number (ℝ)

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.053194
Minimum29.532429
Maximum36.139662
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-09-07T14:43:50.903074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29.532429
5-th percentile31.447373
Q132.341822
median33.082008
Q333.711985
95-th percentile34.594109
Maximum36.139662
Range6.6072335
Interquartile range (IQR)1.370163

Descriptive statistics

Standard deviation0.99256311
Coefficient of variation (CV)0.030029265
Kurtosis0.011861628
Mean33.053194
Median Absolute Deviation (MAD)0.69453534
Skewness-0.032174807
Sum16526.597
Variance0.98518153
MonotonicityNot monotonic
2023-09-07T14:43:51.105787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.49726773 1
 
0.2%
32.83789305 1
 
0.2%
32.34279623 1
 
0.2%
34.46151474 1
 
0.2%
31.81642833 1
 
0.2%
32.997459 1
 
0.2%
32.01807401 1
 
0.2%
31.82797906 1
 
0.2%
32.3025531 1
 
0.2%
32.13386241 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
29.53242897 1
0.2%
30.39318454 1
0.2%
30.4925367 1
0.2%
30.57436368 1
0.2%
30.73772037 1
0.2%
30.81620065 1
0.2%
30.83643267 1
0.2%
30.87948434 1
0.2%
30.97167564 1
0.2%
31.04722214 1
0.2%
ValueCountFrequency (%)
36.13966249 1
0.2%
35.86023651 1
0.2%
35.74266981 1
0.2%
35.63085386 1
0.2%
35.53090415 1
0.2%
35.4331653 1
0.2%
35.37187609 1
0.2%
35.03928306 1
0.2%
35.03744996 1
0.2%
34.96760989 1
0.2%

Time_on_App
Real number (ℝ)

Distinct497
Distinct (%)100.0%
Missing3
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean12.049587
Minimum8.5081522
Maximum15.126994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-09-07T14:43:51.302784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.5081522
5-th percentile10.523744
Q111.386776
median11.982045
Q312.752077
95-th percentile13.668065
Maximum15.126994
Range6.6188421
Interquartile range (IQR)1.3653011

Descriptive statistics

Standard deviation0.99559508
Coefficient of variation (CV)0.082624833
Kurtosis0.12243956
Mean12.049587
Median Absolute Deviation (MAD)0.68576388
Skewness-0.083595407
Sum5988.6445
Variance0.99120956
MonotonicityNot monotonic
2023-09-07T14:43:51.491786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.65565115 1
 
0.2%
11.35104901 1
 
0.2%
12.81539265 1
 
0.2%
11.40964462 1
 
0.2%
11.9171157 1
 
0.2%
14.28801459 1
 
0.2%
12.58924056 1
 
0.2%
10.07946345 1
 
0.2%
12.46114744 1
 
0.2%
11.97906148 1
 
0.2%
Other values (487) 487
97.4%
(Missing) 3
 
0.6%
ValueCountFrequency (%)
8.508152176 1
0.2%
8.668349517 1
0.2%
9.316289204 1
0.2%
9.477777608 1
0.2%
9.607314688 1
0.2%
9.82440177 1
0.2%
9.846124909 1
0.2%
9.953995006 1
0.2%
9.954975969 1
0.2%
9.984514397 1
0.2%
ValueCountFrequency (%)
15.12699429 1
0.2%
14.71538754 1
0.2%
14.42649105 1
0.2%
14.32565494 1
0.2%
14.28801459 1
0.2%
14.22097911 1
0.2%
14.13289346 1
0.2%
14.12178384 1
0.2%
14.06938234 1
0.2%
14.03986726 1
0.2%

Time_on_Website
Real number (ℝ)

Distinct499
Distinct (%)100.0%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean37.05886
Minimum33.913847
Maximum40.005182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-09-07T14:43:51.683167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.913847
5-th percentile35.462146
Q136.346012
median37.06709
Q337.714247
95-th percentile38.777222
Maximum40.005182
Range6.0913344
Interquartile range (IQR)1.3682355

Descriptive statistics

Standard deviation1.0108799
Coefficient of variation (CV)0.027277686
Kurtosis-0.097591541
Mean37.05886
Median Absolute Deviation (MAD)0.68881895
Skewness0.015910349
Sum18492.371
Variance1.0218782
MonotonicityNot monotonic
2023-09-07T14:43:51.876198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.57766802 1
 
0.2%
35.92159519 1
 
0.2%
35.77778217 1
 
0.2%
37.76668677 1
 
0.2%
36.77386137 1
 
0.2%
37.33224075 1
 
0.2%
38.07066426 1
 
0.2%
37.42899737 1
 
0.2%
38.26906069 1
 
0.2%
39.2488039 1
 
0.2%
Other values (489) 489
97.8%
ValueCountFrequency (%)
33.91384725 1
0.2%
34.47687763 1
0.2%
34.48718475 1
0.2%
34.57402759 1
0.2%
34.64980005 1
0.2%
34.7797505 1
0.2%
34.81063145 1
0.2%
34.84561239 1
0.2%
34.89198327 1
0.2%
34.89409275 1
0.2%
ValueCountFrequency (%)
40.00518164 1
0.2%
39.67259096 1
0.2%
39.60037647 1
0.2%
39.57766802 1
0.2%
39.29404346 1
0.2%
39.25293095 1
0.2%
39.2488039 1
0.2%
39.24096484 1
0.2%
39.22071295 1
0.2%
39.13109673 1
0.2%

Length_of_Membership
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5334616
Minimum0.26990109
Maximum6.9226893
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-09-07T14:43:52.066199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.26990109
5-th percentile1.8080268
Q12.9304497
median3.533975
Q34.1265019
95-th percentile5.0813927
Maximum6.9226893
Range6.6527882
Interquartile range (IQR)1.1960522

Descriptive statistics

Standard deviation0.9992775
Coefficient of variation (CV)0.28280412
Kurtosis0.34900979
Mean3.5334616
Median Absolute Deviation (MAD)0.59694473
Skewness-0.10660805
Sum1766.7308
Variance0.99855553
MonotonicityNot monotonic
2023-09-07T14:43:52.261168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.082620633 1
 
0.2%
1.823595183 1
 
0.2%
3.872432042 1
 
0.2%
4.350887844 1
 
0.2%
2.964497876 1
 
0.2%
2.804013693 1
 
0.2%
2.61816531 1
 
0.2%
2.974736815 1
 
0.2%
3.53286158 1
 
0.2%
3.349245383 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
0.26990109 1
0.2%
0.7895199079 1
0.2%
0.80151572 1
0.2%
0.9364975973 1
0.2%
0.9686221157 1
0.2%
1.084585303 1
0.2%
1.13047696 1
0.2%
1.139093538 1
0.2%
1.200483857 1
0.2%
1.228112423 1
0.2%
ValueCountFrequency (%)
6.922689335 1
0.2%
6.401228838 1
0.2%
6.115198946 1
0.2%
6.076653638 1
0.2%
5.976768126 1
0.2%
5.840505876 1
0.2%
5.705940717 1
0.2%
5.705153971 1
0.2%
5.566384892 1
0.2%
5.493507201 1
0.2%

Yearly_Amount_Spent
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.31404
Minimum256.67058
Maximum765.51846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-09-07T14:43:52.453170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum256.67058
5-th percentile376.28998
Q1445.03828
median498.88788
Q3549.31383
95-th percentile628.15325
Maximum765.51846
Range508.84788
Interquartile range (IQR)104.27555

Descriptive statistics

Standard deviation79.314782
Coefficient of variation (CV)0.15884749
Kurtosis0.46397553
Mean499.31404
Median Absolute Deviation (MAD)51.812862
Skewness0.034790184
Sum249657.02
Variance6290.8346
MonotonicityNot monotonic
2023-09-07T14:43:52.645193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
587.951054 1
 
0.2%
445.0621855 1
 
0.2%
486.0834255 1
 
0.2%
592.6884532 1
 
0.2%
501.1224915 1
 
0.2%
476.1392469 1
 
0.2%
357.7831107 1
 
0.2%
440.0027475 1
 
0.2%
478.6009159 1
 
0.2%
443.4418601 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
256.6705823 1
0.2%
266.0863409 1
0.2%
275.9184207 1
0.2%
282.4712457 1
0.2%
298.7620079 1
0.2%
302.1895478 1
0.2%
304.1355916 1
0.2%
308.5277466 1
0.2%
314.4385183 1
0.2%
319.9288698 1
0.2%
ValueCountFrequency (%)
765.5184619 1
0.2%
744.2218671 1
0.2%
725.5848141 1
0.2%
712.3963268 1
0.2%
708.9351849 1
0.2%
700.9170916 1
0.2%
689.7876042 1
0.2%
689.2356998 1
0.2%
684.163431 1
0.2%
669.9871405 1
0.2%

Interactions

2023-09-07T14:43:17.559379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:41:17.552454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:41:46.884378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:42:15.923378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:42:45.194348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:43:24.287372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:41:23.325347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:41:52.791378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:42:21.984379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:42:52.078348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:43:30.445347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:41:29.287348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:41:58.115348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:42:28.107378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:42:58.452376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:43:36.285363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:41:35.158376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:42:04.020348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:42:33.437380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:43:05.139349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:43:42.721348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:41:40.948377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:42:09.973348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:42:39.214377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T14:43:11.133349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-07T14:43:52.765200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Avg._Session_LengthTime_on_AppTime_on_WebsiteLength_of_MembershipYearly_Amount_Spent
Avg._Session_Length1.0000.020-0.0290.0280.336
Time_on_App0.0201.0000.0200.0100.411
Time_on_Website-0.0290.0201.000-0.0210.000
Length_of_Membership0.0280.010-0.0211.0000.780
Yearly_Amount_Spent0.3360.4110.0000.7801.000

Missing values

2023-09-07T14:43:48.078382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-07T14:43:48.266381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-07T14:43:48.576348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

EmailAddressAvatarAvg._Session_LengthTime_on_AppTime_on_WebsiteLength_of_MembershipYearly_Amount_Spent
1mstephenson@fernandez.com835 Frank Tunnel\nWrightmouth, MI 82180-9605Violet34.4972677251122912.65565114916675239.577668019526164.082620632952961587.9510539684005
2hduke@hotmail.com4547 Archer Common\nDiazchester, CA 06566-8576DarkGreen31.92627202636015611.10946072868256437.2689588682977442.66403418213262392.2049334443264
3pallen@yahoo.com24645 Valerie Unions Suite 582\nCobbborough, DC 99414-7564Bisque33.00091475564267511.33027805777751237.110597442120854.104543202376424487.54750486747207
4riverarebecca@gmail.com1414 David Throughway\nPort Jason, OH 22070-1220SaddleBrown34.3055566297555413.71751366514250836.721282677903133.1201787827480914581.8523440352178
5mstephens@davidson-herman.com14023 Rodriguez Passage\nPort Jacobville, PR 37242-1057MediumAquaMarine33.3306725236463912.79518855107811437.536653300594734.446308318351435599.4060920457634
6alvareznancy@lucas.biz645 Martha Park Apt. 611\nJeffreychester, MN 67218-7250FloralWhite33.8710378793419812.02692533975505834.476877629250545.493507201364199637.102447915074
7katherine20@yahoo.com68388 Reyes Lights Suite 692\nJosephbury, WV 92213-0247DarkSlateBlue32.0215955013870111.36634830971052636.6837761528696054.6850172465709115521.5721747578274
8awatkins@yahoo.comUnit 6538 Box 8980\nDPO AP 09026-4941Aqua32.73914293838032612.3519589730029337.3733588585475544.4342734348999375549.9041461052942
9vchurch@walter-martinez.com860 Lee Key\nWest Debra, SD 97450-0495Salmon33.9877728956856413.38623527567643437.5344973415557353.2734335777477144570.2004089636195
10bonnie69@lin.bizPSC 2734, Box 5255\nAPO AA 98456-7482Brown31.936548618448914NaN37.145168223528193.202806071553459427.19938489532814
EmailAddressAvatarAvg._Session_LengthTime_on_AppTime_on_WebsiteLength_of_MembershipYearly_Amount_Spent
491brian28@sanchez.org7446 Mary Ferry\nLake Sherryfurt, GA 49066-0207GhostWhite34.6955911911186611.60899707087546237.684877275306063.1630919310737573510.40138845250476
492leonardhancock@hotmail.com64147 Alexander Station Apt. 474\nEast Jasonview, MN 83788SeaShell34.3439219084275811.69305819707265236.8129341255014643.4470928961368963510.50147847479735
493davidsonkathleen@gmail.com70128 Zimmerman Overpass\nRobertsshire, VA 59860DarkBlue33.68093694960615611.20156988468446737.835447727412182.208813678005546403.8195198321978
494nathan84@lowery.net01242 Stephanie Ways Suite 003\nChurchville, MO 35617MediumSeaGreen32.0609143984100612.62543264205395135.5391424275050845.412357839551379627.603318713015
495kellydeborah@chan.biz354 Sanchez Wall Suite 884\nJuliabury, VI 39735DarkTurquoise33.43109710248713613.3506316844593737.965971622751342.768851943263104510.661792219672
496lewisjessica@craig-evans.com4483 Jones Motorway Suite 872\nLake Jamiefurt, UT 75292Tan33.2376599843672713.56615961308760536.4179847963967943.7465729731034663573.8474377162277
497katrina56@gmail.com172 Owen Divide Suite 497\nWest Richard, CA 19320PaleVioletRed34.7025289728615811.69573629348132837.1902677104525953.5765259152594644529.0490041294304
498dale88@hotmail.com0787 Andrews Ranch Apt. 633\nSouth Chadburgh, TN 56128Cornsilk32.6467766806068911.49940906100208338.332576331960444.958264472618698551.6201454762476
499cwilson@hotmail.com680 Jennifer Lodge Apt. 808\nBrendachester, TX 05000-5873Teal33.32250105130546612.39142299111831836.8400857297670042.336484668112853456.469510066298
500hannahwilson@davidson.com49791 Rachel Heights Apt. 898\nEast Drewborough, OR 55919-9528DarkMagenta33.7159809184498612.41880832475391135.7710161916129652.735159567082275497.7786422156802